In recent years, with the development of technologies and the transition of people's concept, online shopping gradually becomes one of the main channels for shopping. Online shopping platforms have been well developed. Under such circumstances, online shopping platforms have accumulated a large amount of product images. How to effectively organize, analyze, retrieve, and display these product images to consumers has become very important.
The contents of a product image include a main subject and a background. When a user uploads an image of a product and hopes to search for the same or similar products, the user is mainly concerned with the product. The existence of the background may affect the search result for the product. Therefore, it has become an important task to extract the main subject of a product image. Traditional methods for extracting a main subject of an image are based on manual intervention. That is, the segmented regions have to be frame selected and set manually, which has low efficiency. Thus, they are not suitable for hundreds of millions of images on the Internet. Therefore, it is required to design a method for automatically extracting a main subject of a product image, which can accurately extract a specific content in the image.